In search of member value

In today’s increasingly competitive financial services industry, member profitability has become a vital topic. Accompanying this growing interest has been the challenge of developing and interpreting member profitability data and information. For all the talk about retaining the most profitable members and migrating the less profitable ones, few financial institutions can actually calculate profitability at the member level with any degree of accuracy. In fact, a number of financial industry studies indicate that less than one-fifth of U.S. financial institutions accurately calculate member profitability.

Member profitability models provide a powerful means for linking marketing and growth strategies to “bottom-line” results. The integration of financial and transactional data into a consolidated data warehouse that allows management to focus on the value of member relationships in addition to the more traditional behavioral and demographic perspectives offered by some MCIF and CRM systems. However, navigating through the various technical and data issues related to derive accurate and usable profitability can be confusing and intimidating enough to stop many leaders.

For years, traditional MCIF systems have followed various approaches in calculating member profitability, but very few have performed this calculation with any degree of accuracy. Many profit models follow a “one size fits all” approach, which is certainly easier to implement, yet fails to capture critical differences in value among members. The increased emphasis on measuring true “member relationship” value has created the demand for the next generation of business intelligence platforms to improve their capabilities in this area. To help support the development of more value-focused marketing and retention strategies, the profitability model must accurately capture the profitability drivers of each member relationship.

Best practice approaches for deriving account profitability and aggregating to the member level will contain the following key components: 1) historical funds transfer pricing; 2) non-interest income and non-interest expense calculations based on member-specific transaction activity and 3) loan loss provision that reflects the inherent risk of each member. While the level of precision will vary depending on the availability of the data sources – including account-level transaction detail – the main objective should be to reflect specific member behavior in the calculation of member relationship value.

As competition for identifying, retaining and growing the “ideal” members intensifies, it is no longer enough to settle for a rough idea of relative profitability. Successful marketing strategies must create opportunities to serve member needs in ways that will be profitable to the institution. Accurate member profitability information is essential to identifying where those opportunities exist.

Bill Hasapidis

Bill Hasapidis

Bill Hasapidis is Director of Data Analytics at Weber Marketing Group. Bill is the creator of ProfiTRAC™ a comprehensive profitability model that uses customized rule-based algorithms and integrates funds transfer ... Web: Details

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